personalized-recommendation-tool-learning-via-auto

name	personalized-recommendation-tool-learning-via-auto
description	Skill generated from arXiv paper 2607.19739: Personalized Recommendation Tool Learning via Autonomous Language Agents
metadata	{"arxiv":{"id":"2607.19739","title":"Personalized Recommendation Tool Learning via Autonomous Language Agents","authors":["Mingdai Yang","Zhiwei Liu","Weizhi Zhang","Yibo Wang","Hao Peng","Philip Yu"],"published":"2026-07-22","categories":["cs.IR","cs.AI"],"url":"https://arxiv.org/abs/2607.19739","utility":1}}

Personalized Recommendation Tool Learning via Autonomous Language Agents

arXiv: 2607.19739
Published: 2026-07-22
Authors: Mingdai Yang, Zhiwei Liu, Weizhi Zhang, Yibo Wang, Hao Peng, Philip Yu
Categories: cs.IR, cs.AI
Utility: 1.00

Key Innovation

Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks. To circumvent these limitations through architectural design rather than modifying the LLM itself, we propose an agent-based recommendation framework, memory-based $\textbf{P}$ersonalize...

Potential Application

This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.IR, cs.AI.

References

• arXiv: https://arxiv.org/abs/2607.19739